Registration of retinal images from Public Health by minimising an error between vessels using an affine model with radial distortions

In order to estimate a registration model of eye fundus images made of an affinity and two radial distortions, we introduce an estimation criterion based on an error between the vessels. In [1], we estimated this model by minimising the error between characteristics points. In this paper, the detected vessels are selected using the circle and ellipse equations of the overlap area boundaries deduced from our model. Our method successfully registers 96 % of the 271 pairs in a Public Health dataset acquired mostly with different cameras. This is better than our previous method [1] and better than three other state-of-the-art methods. On a publicly available dataset, ours still better register the images than the reference method

Incorporating spatial information for microaneurysm detection in retinal images

The presence of microaneurysms(MAs) in retinal images is a pathognomonic sign of Diabetic Retinopathy (DR). This is one of the leading causes of blindness in the working population worldwide. This paper introduces a novel algorithm that combines information from spatial views of the retina for the purpose of MA detection. Most published research in the literature has addressed the problem of detecting MAs from single retinal images. This work proposes the incorporation of information from two spatial views during the detection process. The algorithm is evaluated using 160 images from 40 patients seen as part of a UK diabetic eye screening programme which contained 207 MAs. An improvement in performance compared to detection from an algorithm that relies on a single image is shown as an increase of 2% ROC score, hence demonstrating the potential of this method

Superimposition of eye fundus images for longitudinal analysis from large public health databases

In this paper, a method is presented for superimposition (i.e. registration) of eye fundus images from persons with diabetes screened over many years for diabetic retinopathy. The method is fully automatic and robust to camera changes and colour variations across the images both in space and time. All the stages of the process are designed for longitudinal analysis of cohort public health databases where retinal examinations are made at approximately yearly intervals. The method relies on a model correcting two radial distortions and an affine transformation between pairs of images which is robustly fitted on salient points. Each stage involves linear estimators followed by non-linear optimisation. The model of image warping is also invertible for fast computation. The method has been validated (1) on a simulated montage and (2) on public health databases with 69 patients with high quality images (271 pairs acquired mostly with different types of camera and 268 pairs acquired mostly with the same type of camera) with success rates of 92% and 98%, and five patients (20 pairs) with low quality images with a success rate of 100%. Compared to two state-of-the-art methods, ours gives better results.Comment: This is an author-created, un-copyedited version of an article published in Biomedical Physics \& Engineering Express. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/2057-1976/aa7d1

Noninvasive Assessment of Photoreceptor Structure and Function in the Human Retina

The human photoreceptor mosaic underlies the first steps of vision; thus, even subtle defects in the mosaic can result in severe vision loss. The retina can be examined directly using clinical tools; however these devices lack the resolution necessary to visualize the photoreceptor mosaic. The primary limiting factor of these devices is the optical aberrations of the human eye. These aberrations are surmountable with the incorporation of adaptive optics (AO) to ophthalmoscopes, enabling imaging of the photoreceptor mosaic with cellular resolution. Despite the potential of AO imaging, much work remains before this technology can be translated to the clinic. Metrics used in the analysis of AO images are not standardized and are rarely subjected to validation, limiting the ability to reliably track structural changes in the photoreceptor mosaic geometry. Preceding the extraction of measurements, photoreceptors must be identified within the retinal image itself. This introduces error from both incorrectly identified cells and image distortion. We developed a novel method to extract measures of cell spacing from AO images that does not require identification of individual cells. In addition, we examined the sensitivity of various metrics in detecting changes in the mosaic and assessed the absolute accuracy of measurements made in the presence of image distortion. We also developed novel metrics for describing the mosaic, which may offer advantages over more traditional metrics of density and spacing. These studies provide a valuable basis for monitoring the photoreceptor mosaic longitudinally. As part of this work, we developed software (Mosaic Analytics) that can be used to standardize analytical efforts across different research groups. In addition, one of the more salient features of the appearance of individual cone photoreceptors is that they vary considerably in their reflectance. It has been proposed that this reflectance signal could be used as a surrogate measure of cone health. As a first step to understanding the cellular origin of these changes, we examined the reflectance properties of the rod photoreceptor mosaic. The observed variation in rod reflectivity over time suggests a common governing physiological process between rods and cones

Generalizable automated pixel-level structural segmentation of medical and biological data

Over the years, the rapid expansion in imaging techniques and equipments has driven the demand for more automation in handling large medical and biological data sets. A wealth of approaches have been suggested as optimal solutions for their respective imaging types. These solutions span various image resolutions, modalities and contrast (staining) mechanisms. Few approaches generalise well across multiple image types, contrasts or resolution. This thesis proposes an automated pixel-level framework that addresses 2D, 2D+t and 3D structural segmentation in a more generalizable manner, yet has enough adaptability to address a number of specific image modalities, spanning retinal funduscopy, sequential fluorescein angiography and two-photon microscopy. The pixel-level segmentation scheme involves: i ) constructing a phase-invariant orientation field of the local spatial neighbourhood; ii ) combining local feature maps with intensity-based measures in a structural patch context; iii ) using a complex supervised learning process to interpret the combination of all the elements in the patch in order to reach a classification decision. This has the advantage of transferability from retinal blood vessels in 2D to neural structures in 3D. To process the temporal components in non-standard 2D+t retinal angiography sequences, we first introduce a co-registration procedure: at the pairwise level, we combine projective RANSAC with a quadratic homography transformation to map the coordinate systems between any two frames. At the joint level, we construct a hierarchical approach in order for each individual frame to be registered to the global reference intra- and inter- sequence(s). We then take a non-training approach that searches in both the spatial neighbourhood of each pixel and the filter output across varying scales to locate and link microvascular centrelines to (sub-) pixel accuracy. In essence, this \link while extract" piece-wise segmentation approach combines the local phase-invariant orientation field information with additional local phase estimates to obtain a soft classification of the centreline (sub-) pixel locations. Unlike retinal segmentation problems where vasculature is the main focus, 3D neural segmentation requires additional exibility, allowing a variety of structures of anatomical importance yet with different geometric properties to be differentiated both from the background and against other structures. Notably, cellular structures, such as Purkinje cells, neural dendrites and interneurons, all display certain elongation along their medial axes, yet each class has a characteristic shape captured by an orientation field that distinguishes it from other structures. To take this into consideration, we introduce a 5D orientation mapping to capture these orientation properties. This mapping is incorporated into the local feature map description prior to a learning machine. Extensive performance evaluations and validation of each of the techniques presented in this thesis is carried out. For retinal fundus images, we compute Receiver Operating Characteristic (ROC) curves on existing public databases (DRIVE & STARE) to assess and compare our algorithms with other benchmark methods. For 2D+t retinal angiography sequences, we compute the error metrics ("Centreline Error") of our scheme with other benchmark methods. For microscopic cortical data stacks, we present segmentation results on both surrogate data with known ground-truth and experimental rat cerebellar cortex two-photon microscopic tissue stacks.Open Acces

Review on retrospective procedures to correct retinal motion artefacts in OCT imaging

Motion artefacts from involuntary changes in eye fixation remain a major imaging issue in optical coherence tomography (OCT). This paper reviews the state-of-the-art of retrospective procedures to correct retinal motion and axial eye motion artefacts in OCT imaging. Following an overview of motion induced artefacts and correction strategies, a chronological survey of retrospective approaches since the introduction of OCT until the current days is presented. Pre-processing, registration, and validation techniques are described. The review finishes by discussing the limitations of the current techniques and the challenges to be tackled in future developments

Structure-Function Correlation of the Human Central Retina

The impact of retinal pathology detected by high-resolution imaging on vision remains largely unexplored. Therefore, the aim of the study was to achieve high-resolution structure-function correlation of the human macula in vivo.To obtain high-resolution tomographic and topographic images of the macula spectral-domain optical coherence tomography (SD-OCT) and confocal scanning laser ophthalmoscopy (cSLO), respectively, were used. Functional mapping of the macula was obtained by using fundus-controlled microperimetry. Custom software allowed for co-registration of the fundus mapped microperimetry coordinates with both SD-OCT and cSLO datasets. The method was applied in a cross-sectional observational study of retinal diseases and in a clinical trial investigating the effectiveness of intravitreal ranibizumab in macular telangietasia type 2. There was a significant relationship between outer retinal thickness and retinal sensitivity (p<0.001) and neurodegeneration leaving less than about 50 µm of parafoveal outer retinal thickness completely abolished light sensitivity. In contrast, functional preservation was found if neurodegeneration spared the photoreceptors, but caused quite extensive disruption of the inner retina. Longitudinal data revealed that small lesions affecting the photoreceptor layer typically precede functional detection but later cause severe loss of light sensitivity. Ranibizumab was shown to be ineffective to prevent such functional loss in macular telangietasia type 2.Since there is a general need for efficient monitoring of the effectiveness of therapy in neurodegenerative diseases of the retina and since SD-OCT imaging is becoming more widely available, surrogate endpoints derived from such structure-function correlation may become highly relevant in future clinical trials

Incorporating spatial and temporal information for microaneurysm detection in retinal images

The retina of the human eye has the potential to reveal crucial information about several diseases such as diabetes. Several signs such as microaneurysms (MA) manifest themselves as early indicators of Diabetic Retinopathy (DR). Detection of these early signs is important from a clinical perspective in order to suggest appropriate treatment for DR patients. This work aims to improve the detection accuracy of MAs in colour fundus images. While it is expected that multiple images per eye are available in a clinical setup, proposed segmentation algorithms in the literature do not make use of these multiple images. This work introduces a novel MA detection algorithm and a framework for combining spatial and temporal images. A new MA detection method has been proposed which uses a Gaussian matched filter and an ensemble classifier with 70 features for the detection of candidates. The proposed method was evaluated on three public datasets (171 images in total) and has shown improvement in performance for two of the sets when compared to a state-of-the-art method. For lesion-based performance, the proposed method has achieved Retinopathy Online Challenge (ROC) scores of 0.3923, 2109 and 0.1523 in the MESSIDOR, DIARETDB1 and ROC datasets respectively. Based on the ensemble algorithm, a framework for the information combination is developed and consists of image alignment, detecting candidates with likelihood scores, matching candidates from aligned images, and finally fusing the scores from the aligned image pairs. This framework is used to combine information both spatially and temporally. A dataset of 320 images that consists of both spatial and temporal pairs was used for the evaluation. An improvement of performance by 2% is shown after combining spatial information. The framework is applied to temporal image pairs and the results of combining temporal information are analyzed and discussed

Ocular higher-order aberrations and visual performance

Since adaptive optics was first used to correct the monochromatic aberrations of the eye over a decade ago there has been considerable interest in correcting the ocular aberrations beyond defocus and astigmatism. In order to understand the prospective benefits of correcting these higher-order aberrations it is important to study their effect on visual performance. From a clinical perspective it is important to know how different types of aberration can affect visual performance so that wavefront measurements can be better understood. Visual performance is determined by a combination of optical and neural factors. It is important to consider how degradations in the optical quality of the eye can impact the neural processes involved in visual tasks such as object recognition. In this thesis we present a study of the effects of three types of aberration, defocus, coma and secondary astigmatism, on letter recognition and reading performance. In the course of this work we also characterise the repeatability of the Zywave aberrometer, which we used to measure our subjects' ocular wavefronts. We use stimuli that have these aberrations applied in their rendering to examine the differences between these aberrations and how they differ with respect to the visual task. We find that secondary astigmatism causes the largest impairment to both letter recognition and reading performance, followed by defocus. Coma causes comparatively smaller degradations to performance but its effect is different depending on the visual task. We can predict the reduction in performance based on a simple cross-correlation model of letter confusability. The relationship between these predictions and the experimental results are the same for all three aberrations, in the case of single letter recognition. In reading however, the relationship is different for coma. We suggest that coma causes lateral masking effects and may additionally disrupt the planning of eye movements. Coma slows reading, but does not specifically impair word identification whereas defocus and secondary astigmatism do. We attribute disruptions in word identification to the dramatic effects defocus and secondary astigmatism have on the form of a letter

Deformable Image Registration in the Analysis of Multiple Sclerosis

In medical image analysis, image registration is the task of finding corresponding features in two or more images, and using them to solve for the transformation that best aligns the images. Knowing the alignment allows information, such as landmarks and functional metrics, to be easily transferred between images, and allows them to be analyzed together. This dissertation focuses on the development of deformable image registration techniques for the analysis of multiple sclerosis (MS), a neurodegenerative disease that damages the myelin sheath of nervous tissue. MS is known to affect the entire central nervous system (CNS), and can result in the loss of sensorimotor control, cognition, and vision. Hence, the four primary contributions of this dissertation are on the development and application of deformable image registration in the three areas of the CNS that are most currently studied for MS -- the spinal cord, the retina, and the brain. First, for spinal cord magnetic resonance imaging (MRI), an approach is presented that uses deformable registration to provide atlas priors for automatic topology-preserving segmentation of the spinal cord and cerebrospinal fluid. The method shows high accuracy and robustness when compared to manual raters, and allows spinal cord atrophy to be analyzed on large datasets without manual segmentations. Second, for spinal cord diffusion tensor imaging, a pipeline is presented that uses deformable registration to correct for susceptibility distortions in the images. The pipeline allows for accurate computation of spinal cord diffusion metrics, which are shown to be significantly correlated with clinical measures of sensorimotor function and disability levels. Third, for optical coherence tomography (OCT) of the retina, a deformable registration technique is presented that constrains the transformation to follow the OCT acquisition geometry. 3D voxel-based analysis using the algorithm found significant differences between healthy and MS cohorts in regions of the retina that is consistent with previous findings using 2D analysis. Lastly, for brain MRI, a multi-channel registration framework is presented that can use distance transforms and image synthesis to improve registration accuracy. Together, these techniques have enabled several types of analysis that were previously unavailable for the study of MS